Group formation and dynamic pricing for E-commerce in social networks

ABSTRACT

A system platform and associated methods are provided for implementing online reverse auctions in a social network platform (SNP). A fully-automated, live, reverse-auction based system is integrated into an SNP to enable buyers to initiate desired transactions and take advantage of a subscribers&#39; network of friends, colleagues, co-workers, family members and connections by connecting buyers and sellers in a non-intrusive, targeted fashion. Sellers compete for buyers&#39; business by providing dynamic, real-time seller-specific pricing while simultaneously optimizing the seller&#39;s target parameters such as price, inventory levels, profit, revenue and volume.

CROSS-REFERENCE TO RELATED APPLICATIONS

This utility patent application is a divisional of and claims priorityfrom U.S. patent application Ser. No. 12/948,311, filed Nov. 17, 2010,titled “SYSTEMS AND METHODS FOR IMPLEMENTING AUCTIONS ON SOCIAL NETWORKPLATFORMS” in the name of Mukesh Chatter, Rohit Goyal, Priti Chatter,and Shiao-Bin Soong, which claims priority to and the benefit of U.S.provisional patent application Ser. Nos. 61/261,997, filed Nov. 17,2009, and 61/266,800, filed Dec. 4, 2009, each entitled “Auctions in aSocial Networking Framework.”

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever. Copyright 2014 Peak Silver Advisors, LLC.

BACKGROUND OF THE INVENTION

Technical Field

This invention relates to computer-based methods and systems forimplementing online auctions, and more specifically to computer-aidedtechniques for implementing and executing online auctions within socialnetwork platforms.

Background Information

On-line social networks strive to enroll as many members as possible. Inmost social networks, each member defines her own group of friends bycompiling a list of friends or colleagues. Each individual typically hasthe ability to define the parameters governing her community of friends,such as who she wants to interact with, who she shares information with,who has access to her personal data and who to request as a friend orconnection. Facebook, LinkedIn, MySpace and Orkut are examples of suchsocial networks. Each person in a member's group of friends typicallyhas her own set of friends, some of whom may overlap with others, andsome which do not. Thus an individual and her group of friends(collectively referred in this document as a “micro-community”) can beunique to each individual. As a result, a social networking site mayconsist of millions of micro-communities, each of varying size andcharacteristics.

Membership is typically free at these sites, and advertisements andpay-to-participate games and applications are commonly used to monetizethe sites. In some cases, a marketplace may also be provided tofacilitate trading and commerce among sellers and subscribers. Suchtransactions are not conducted any differently than conventional onlinetransactions, and are not unique to the social network construct.

While some on-line social networking sites may have upwards of 250million users, these platform providers have not succeeded in monetizingthis substantial traffic apart from conventional advertising. In fact,even the most successful sites (in terms of membership, page views,etc.) are still struggling to attain a break-even cash flow. Some of thechallenges to monetize the subscriber base and traffic on these socialnetworking platforms (“SNPs”) are summarized below in the context of theSNPs themselves, the subscribers (or members), and those looking to selltheir products or services on the sites (“sellers”).

From the perspective of the SNPs, subscribers are inherently moredemanding of privacy and place a premium on it thus limiting theaggressive advertising opportunities. Thus, gaining and maintainingsubscribers' trust is very important and aggressive attempts to pushproducts or services by those outside a subscribers micro-communityencounters a high degree of skepticism and resistance. Moreover, thelack of transparency associated with on-line marketing and behavioraladvertising push erodes trust and is generally not accepted bysubscriber bases. In addition, the content and dialog on SNPs tends tobe more social and less commercial such that conventional socialetiquettes adhered to in personal interactions lead to diminishedattention to or tolerance of commercial messages. Regarding applicationsimplemented on SNPs, the SNPs receive little or no compensation for theuse of applications developed for their platforms. These factors, andothers, have made it difficult for SNPs to capitalize on the high trustinherent within a subscriber's micro-communities and translate it into arevenue opportunity or derive any benefit from activities and sharedknowledge or habits of subscribers within a particular micro-community.

From the perspective of the subscribers, there is an inherent trustamong members of her micro-community that is far greater than an outsideadvertiser, and members of a micro-community are often interested insimilar products or services and share their experiences. While groupbuying sites like BuyWithMe and GroupOn allow for community buying,subscribers are more apt to share their experiences with members oftheir micro-community and trust recommendations and reviews from othersin their community, as opposed to the general public. Moreover, the“deals” that are available on conventional group buy sites aredetermined by the merchants and the sites themselves (e.g., the “deal ofthe day”) as opposed to deals that are initiated and defined by membersof the community itself. As such, subscribers cannot initiate atransaction for the products and/or services she desires when she needsit.

From the perspective of the sellers, conventional advertisementstargeted at a micro-community are not effective because aggressiveattempts to push products and services by those outside one's own uniquemicro-community encounters higher degree of skepticism and resistance. Alack of transparency associated with aggressive marketing/advertisingpush is generally not accepted by the subscriber base and the ROI forthis type of online ad spending is questionable. Furthermore, sellershave no way to reach members of a micro-community if one member of thatcommunity had a positive interaction with an ad.

Therefore, what is needed is a new approach online advertising andselling products and services that takes advantage of themicro-communities inherent in social networks.

SUMMARY

Embodiments of the invention provides a novel system platform andtechnique for implementing online auctions in a social network platform(SNP). A fully-automated, live, reverse-auction based system isintegrated into an SNP to enable buyers to initiate desired transactionsat will and to take advantage of a subscribers' network of friends,colleagues, co-workers, family members and connections (commonlyreferred-to herein as a subscribers' “micro-community”) by connectingbuyers and sellers in a non-intrusive, targeted fashion. Sellers cancompete for buyers' business by providing dynamic, real-timeseller-specific pricing while simultaneously optimizing the seller'starget parameters such as price, inventory levels, profit, revenue andvolume, all at customer acquisition costs significantly lower thanconventional targeted advertising methods. Subscribers have access totheir own unique “boards”—online screens or dashboards—that displayreal-time auction and transaction information across the entireplatform. Through these dashboards, subscribers can view deals as theypropagate through the SNP, providing opportunities for the SNP provider,brands and other commercial enterprises to display ads and otherrevenue-generating content to the subscribers.

Accordingly, in a first aspect, a computerized method for providing anonline reverse auction within a social network platform includes thefollowing processes and functions. At an application server, anelectronic message including initial auction parameters of a desiredpurchase is received from a subscriber to a social network platform.Based on the initial parameters, an auction controller calculatesauction parameters and provides the auction parameters to a set ofautomated auction engines, each being associate with a seller capable offulfilling the purchase. Offers to fulfill the desired purchase for thesubscriber are received from the auction engines in an iterativefashion, where each iteration includes programmatically-calculatedupdated auction parameters that are increasingly favorable to thesubscriber. This continues until a single seller has provided a mostfavorable set of auction parameters. The most favorable auctionparameters are distributed over an electronic network to additionalsubscribers of the social network platform, thus allowing the additionalsubscribers to complete a purchase using these same auction parameters.

The initial parameters may include parameters such as a description ofan item or service that the subscriber desires to purchase, a quantityof the item, a desired class of seller (based, for example, on customerreviews, satisfaction ratings, industry ratings, etc.), delivery optionsand/or a return policy. In certain embodiments, the duration duringwhich the additional subscribers may take advantage of the mostfavorable parameters and complete a subsequent transaction is limited.In other cases, the subsequent transactions may be limited based oninventory levels of the sellers. It may be limited, for example, by theseller, or, in some cases, a system parameter. The updated auctionparameters may include a price at which the sellers are willing tofulfill the desired purchase, as well as modifications to one or more ofthe initial parameters. During each iteration each of the sellersrevises its respective updated auction parameters based on aseller-specific objective function. The objective function may beconstrained by a number of seller-specific parameters such as an actualor desired inventory level, profit and revenue targets, supplier'sbreaks, customer acquisition cost, advertising and promotion costs, someof which may change over time.

In some embodiments, the auction engines may alter the duration that thetransaction is available to additional subscribers, and/or the number ofunits that particular seller is willing to allocate to the transactions.Such alterations may be based, for example, on the number of additionalsubscribers joining the transaction, or other transactions in which thesame seller is participating, all constrained by the seller's uniquetargets. In addition to optimization of the seller's unique targets(such as monthly profit, revenue, inventory, supplier's break, promotioncost, advertising cost etc.) while computing price for a specifictransaction, factors taken into consideration may also include anaggregated amount of deal validity duration left across all transactionsin which the seller is participating, cumulative conversion ratesachieved across all transactions, variance from expected conversionrates as a function of time, and the total number of units sold. Thisanalysis may be performed across multiple independently initiatedtransactions for the same product, across product lines from the samemanufacturer, and across different manufacturers.

Consumer profiles may be derived for subscribers to the social networkplatform. The profiles may be based on consumer demographic and/orbehavioral traits such as age, income, gender, education, location,historical purchase patterns, expected conversion rates, size of thesubscribers community within the social network platform, activity levelwithin the platform, as well as others. The profile may also be used asinput into the objective function, further influencing the seller'sobjective function and, as a result, the updated auction parameters.

The additional subscribers may, in some instances, be members of thesubscriber's community within the social network platform. The communitymay include additional subscribers that are one, two or even moredegrees of separate from the subscriber. In some cases, the degree ofseparation may be configurable and/or selectable by the subscriber. Thesubscribers may also utilize a filtering function that facilitatessearching across all potential purchases according to one or more of theauction parameters, and, in some cases, permits additional subscribersnot connected to the subscriber to participate in the transaction.

Information about the transaction may be presented to the subscriber andthe additional subscribers using an electronically renderable“dashboard” or screen that may, in different forms, include the initialauction parameters, the updated auction parameters, and/or the mostfavorable set of auction parameters. The screen may be propagated toadditional subscribers based on a traversal of connections within thesocial network platform until a stopping criteria is met, such as theexpiration of a deal duration, depletion of inventory, propagation to amaximum number of subscribers etc.

In some embodiments the most favorable set of auction parameters are notsent to the additional subscribers until the subscriber has completed atransaction at the same parameters. In such cases, the applicationserver may also receive messages from the subscriber and/or theadditional subscribers indicating a desire to complete a transactionbased on the most favorable set of auction parameters.

In another aspect, a computerized method for facilitating the sale ofgoods to subscribers to a social network platform includes the followingprocesses and functions. Parameters of a completed transaction between asubscriber to the platform and a seller are electronically propagated byan application server to additional subscribers to the platform. Theadditional subscribers are members of the subscriber's community—e.g.,they are connected to the subscriber by one, two or more degrees ofseparation. At the application server, indications of interest incompleting subsequent transactions are received from the additionalsubscribers, and, upon such receipt, the seller iteratively determineswhether they can fulfill each subsequent transaction based on theparameters of the completed transaction, as well as theirseller-specific objective function as constrained by variousseller-specific parameters, such as an actual or desired inventorylevel, number of additional subscribers requesting to take part in thetransaction, revenue and profit targets, seller's break levels, as wellas others.

In some cases, the iterative determination occurs after receiving eachindication of interest. In other cases, the iterations occur afterreceiving a group of indications of interest (e.g., after every tenrequests to join the transaction), which may, in some cases, whenaggregated together, exceed one or more of the seller-specificparameters such that the seller can no longer fulfill the subsequentrequests.

Consumer profiles may be derived for subscribers to the social networkplatform. The profiles may be based on consumer demographic and/orbehavioral traits such as age, income, gender, education, location,historical purchase patterns, expected conversion rates, size of thesubscribers community within the social network platform, activity levelwithin the platform, as well as others. The profile may also be used asinput into the objective function, further influencing the seller'sobjective function and, as a result, the updated auction parameters.

The additional subscribers may, in some instances, be members of thesubscriber's community within the social network platform. The communitymay include additional subscribers that are one, two or even moredegrees of separate from the subscriber. In some cases, the degree ofseparation may be configurable and/or selectable by the subscriber. Thesubscribers may also utilize a filtering function that facilitatessearching across all potential purchases according to one or more of theauction parameters, and, in some cases, permits additional subscribersnot connected to the subscriber to participate in the transaction.

For each iteration, the auction engine can compute dynamic pricing byoptimizing the seller's objective function based on the seller's uniquetargets. The optimization may also consider the number of additionalsubscribers who have joined the transaction, the total volume requested,how many times the commitment price has been met, the difference betweencommitment price and the dream price, a profile of each community andtheir respective expected conversion rate, and blended communityprofiles derived from the subscribers. In some embodiments, each auctionengine scans the dream prices across various proposed transactions forthe same product and performs real-time optimized pricing computationtaking each transaction into consideration, and subsequently decides toproceed or not. Additional consideration may also include scanningtransactions for different products. In some cases, the group ofsubscribers can pre-form (prior to any initial auction by a singlesubscriber) and initiate the process as a group instead of triggeringsubsequent iterations as each additional subscriber joins.

Information about the transaction may be presented to the subscriber andthe additional subscribers using an electronically renderable“dashboard” or screen that may, in different forms, include the initialauction parameters, the updated auction parameters, and/or the mostfavorable set of auction parameters. The screen may be propagated toadditional subscribers based on a traversal of connections within thesocial network platform until a stopping criteria is met, such as theexpiration of a deal duration, depletion of inventory, propagation to amaximum number of subscribers etc.

The process may repeat iteratively after each completed subsequenttransaction. As a result, each additional subscriber replaces thesubscriber in the process, thus propagating the parameters of thecompleted subsequent transactions between the seller and the additionalsubscribers to subsequent subscribers, each subsequent subscriber beinga member of one of the additional subscriber's community within thesocial network platform. In such cases, certain auction parameters maychange as the transaction propagates to subsequent subscribers, based onthe seller's ability to meet the terms of the transaction and stillsatisfy his seller-specific objectives.

After the final iteration is completed for the group, the final groupprice may be offered to each subscriber's community, along with theavailable inventory and deal validity duration. In such cases, eachauction engine computes number of units to be offered and correspondingdeal validity duration by optimizing inventory, profit, revenue,supplier's break, acquisition cost and other such seller targets, inconjunction with additional factors such as community profile of each ofthe corresponding subscriber in the group and their respective expectedconversion rate. Each additional transaction may, in turn, result insubsequent propagation to the additional subscribers.

In some cases, the seller may also consider the effects of multiple,independent transactions initiated by different subscribers whendetermining his optimal bid. The independent transactions may be for thesame or similar products, or, in some cases, cross product lines or evensuppliers.

In yet another aspect, a computerized method for facilitating the salesof goods to subscribers to a social network platform includes thefollowing processes. An electronic message is received at an applicationserver, the message being from a subscriber to a social network platformand including desired auction parameters of a proposed purchase of anitem and a final auction time. The desired auction parameters aretransmitted over an electronic network to an initial set of automatedauction engines, each of which is associated with a respective seller.Offers to fulfill the proposed purchase are iteratively received fromsome of the initial set of auction engines, wherein each iterationcomprises programmatically-calculated updated auction parametersincreasingly favorable to the subscriber until at least one engine hasprovided a set of auction parameters. The set of auction parameters aretransmitted to additional subscribers to the social network platform,thus allowing the additional subscribers to respond with respectiveindications of interest in joining the proposed purchase. Upon receivingindications of interest from the subset of the additional subscribers,an updated set of automated auction engines, each being associated witha respective seller, is notified of the indications of interest to jointhe proposed purchase. In response offers are received from the updatedset of auction engines to fulfill the proposed purchase, wherein eachiteration includes programmatically-calculated interim auctionparameters increasingly favorable to the additional subscribers andsatisfying a seller-specific objective function constrained by one ormore seller-specific parameters, until a stopping criteria is met.

Examples of seller-specific parameters include, but not necessarilylimited to an actual or desired inventory level, supplier break points,a revenue target and/or a profit target, actual revenue, actual profit,customer acquisition costs, etc. In some cases, the seller-specificobjective function is further constrained by the number of receivedindications of interest to join the proposed purchase.

The stopping criteria may be, for example, reaching the final auctiontime or reaching a “dream price”—a price at which the subscriber notedshe would definitely complete the transaction. In some cases, the dreamprice is included in the desired auction parameters and provided to theauction engines, whereas in other cases it remains hidden from theauction engines. The auction parameters (including the desiredparameters) may also include a commitment price, at which any subscriber(including a subsequent subscriber) expressing interest in thetransaction is bound to complete the transaction if the final price isat or below the commitment price.

The auction parameters may include, as examples, a description of theitem to be purchased, a quantity of the item to be purchased, a numberof the additional subscribers that may join the proposed purchase,recent price data for similar products, desired characteristics of theadditional subscribers that may join the proposed purchase, a desiredclass of seller, delivery options and a return policy.

In some cases, the transactions may be consummated between a sellerassociated with the winning auction engine and the subset of theadditional subscribers according to a final set of auction parameters,where the final set of auction parameters is the most favorable set ofparameters to the additional subscribers offered by one of the updatedset of auction engines. Notification to the updated set of automatedauction engines may occur after receipt of each indication of interestto join the purchase, or in some cases, after receipt of a predefinednumber of indications of interest to join the purchase.

Consumer profiles may be derived for subscribers to the social networkplatform. The profiles may be based on consumer demographic and/orbehavioral traits such as age, income, gender, education, location,historical purchase patterns, expected conversion rates, size of thesubscribers community within the social network platform, activity levelwithin the platform, as well as others. The profile may also be used asinput into the objective function, further influencing the seller'sobjective function and, as a result, the updated auction parameters. Insome instances, a community profile may be derived by aggregating and/oraveraging certain aspects of individual profiles such that an entirecommunity's profile influences the seller's objective function.

The additional subscribers may, in some instances, be members of thesubscriber's community within the social network platform. The communitymay include additional subscribers that are one, two or even moredegrees of separate from the subscriber. In some cases, the degree ofseparation may be configurable and/or selectable by the subscriber. Thesubscribers may also utilize a filtering function that facilitatessearching across all potential purchases according to one or more of theauction parameters, and, in some cases, permits additional subscribersnot connected to the subscriber to participate in the transaction.

Information about the transaction may be presented to the subscriber andthe additional subscribers using an electronically renderable“dashboard” or screen that may, in different forms, include the initialauction parameters, the updated auction parameters, and/or the mostfavorable set of auction parameters. The screen may be propagated toadditional subscribers based on a traversal of connections within thesocial network platform until a stopping criteria is met, such as theexpiration of a deal duration, depletion of inventory, propagation to amaximum number of subscribers etc.

In another aspect, a system for facilitating an online reverse auctionwithin a social network platform includes multiple auction engines, eachauction engine being associated with a potential seller of goods orservices and an application server, which in turn includes an auctioncontroller and communication server in communication with the auctionengines over an electronic network. The communication server isconfigured to receive an electronic message from a subscriber to asocial network platform that includes initial auction parameters of adesired purchase and provide the initial auction parameters to theauction engines. The communication server is also configured toiteratively receive offers to fulfill the desired purchase for thesubscriber from the auction engines. Each iteration comprisesprogrammatically-calculated updated auction parameters increasinglyfavorable to the subscriber until a single auction engine has provided amost favorable set of auction parameters. The communication server alsodistributes over the electronic network the most favorable set ofauction parameters to additional subscribers of the social networkplatform, thus allowing the additional subscribers to complete apurchase according to the most favorable set of auction parameters. Theauction controller is configured to determine the additional subscribersof the social network platform to whom the most favorable set of auctionparameters are to be distributed.

In some embodiments, the communication server also receives a messagefrom the subscriber and/or additional subscribers indicating a desire tocomplete the desired purchase at the most favorable set of auctionparameters. The auction controller may also be configured to limit theduration during which the additional subscribers may complete thepurchase, and, in some cases, determine the additional subscribers towhich the most favorable set of auction parameters are to be distributedby traversing relationships among subscribers to the social networkplatform. For example, the auction controller may limit the degree ofseparation between the subscriber and the additional subscribers.

In some implementations of the system, the auction engines areconfigured to iteratively revise their respective updated auctionparameters based on a seller-specific objective function which isconstrained by seller-specific parameters such as an actual or desiredinventory level, supplier break, revenue target and/or profit target.Further, the communication server may access and provide consumerprofiles (of the subscriber and/or the additional subscribers) to theauction engines such that the programmatically-calculated updatedauction parameters are based, at least in part, on the consumerprofiles, which may include the subscriber's age, income, gender,education, location, historical purchase patterns, sales conversionrate, customer acquisition cost and activity level within the socialnetwork platform. In some instances, a community profile may be derivedby aggregating and/or averaging certain aspects of individual profilessuch that an entire community's profile influences the seller'sobjective function.

The communications server may also be configured to provide to thesubscriber and each of the additional subscribers an electronicallyrendered screen that includes the initial auction parameters, theauction parameters, the updated auction parameters and/or the mostfavorable set of auction parameters.

In yet another aspect, a system for facilitating sales of goods tosubscribers to a social network platform includes an application serverand multiple automated auction engines, each of which is associated witha respective seller. The application server is configured to receive anelectronic message from a subscriber to a social network platform thatincludes desired auction parameters of a proposed purchase of an item,including a final auction time and provide the desired auctionparameters to an initial set of the automated auction engines. Theapplication server iteratively receives offers to fulfill the proposedpurchase from the auction engines, wherein each iteration comprisesprogrammatically-calculated updated auction parameters increasinglyfavorable to the subscriber until at least one engine has provided a setof auction parameters. The set of auction parameters are distributedover an electronic network to additional subscribers to the socialnetwork platform, thus allowing the additional subscribers to respondwith respective indications of interest in joining the proposedpurchase. The application server receives indications of interest tojoin the proposed purchase from at least a subset of the additionalsubscribers, and upon receipt of the indications of interest from thesubset of the additional subscribers notifies an updated set ofautomated auction engines of the indications of interest to join theproposed purchase. The server iteratively receives offers to fulfill theproposed purchase from an updated set of auction engines, wherein eachiteration comprises programmatically-calculated interim auctionparameters increasingly favorable to the additional subscribers andsatisfying a sell-specific objective function constrained by one or moreseller-specific parameters, until a stopping criteria is met. Theapplication server then identifies a winning auction engine based on thereceived offers.

In another aspect, a hardware apparatus architected for facilitating anonline reverse auction within a social network platform includes a cellmemory, a rules memory, a controller and a processor. The cell memoryincludes grouped memory addresses, each grouping representing asubscriber to a social network and storing information describingpotential purchases available to and/or initiated by the subscriber. Therules memory is configured to store instructions for implementing rulesgoverning the propagation and updating of information stored in the cellmemory. The controller reads information from the cell memory and rulesmemory and apply the rules to the information, and the processorconfigures the cell memory and rules memory.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the invention.

FIG. 1 is a block diagram of an embodiment of an online reverse auctionenvironment according to various embodiments of the invention.

FIG. 2 is a block diagram of an embodiment of a server in the auctionsystem of FIG. 1.

FIG. 3 illustrates certain interactions among participants in a reverseauction environment according to various embodiments of the invention.

FIG. 4 illustrates the propagation of deal boards for subscribers to asocial network participating in online auctions within a subscribersimmediate community according to various embodiments of the invention.

FIG. 5 is a flow chart depicting the extension of an online auction toadditional members of an online community according to variousembodiments of the invention.

FIG. 6 illustrates the propagation of deals across a social networkplatform according to one embodiment of the invention.

FIG. 7 illustrates the propagation of deal boards for subscribers to asocial network participating in online auctions across multiplesubscribers' communities according to various embodiments of theinvention.

FIG. 7A illustrates the application of various optimization functionsacross multiple transactions for different products according to variousembodiments of the invention.

FIG. 8 illustrates one example of a deal board according to variousembodiments of the invention.

FIG. 9 illustrates one example of a volume board according to variousembodiments of the invention.

FIG. 10 is a flow chart showing the steps for initiating a new dealaccording to various embodiments of the invention.

FIG. 11 is a flow chart showing the steps for joining a deal accordingto various embodiments of the invention.

FIG. 12 is a flow chart showing the steps for cancelling a dealaccording to various embodiments of the invention.

FIG. 13 is a flow chart showing the steps for unjoining a deal accordingto various embodiments of the invention.

FIG. 14 is a flow chart showing the effects of subscriber connecting toanother subscriber according to various embodiments of the invention.

FIG. 15 is a flow chart showing the effects of subscriber disconnectingfrom another subscriber according to various embodiments of theinvention.

FIG. 16 illustrates a cell-based representation of the relationshipsamong subscribers in a social network platform according to variousembodiments of the invention.

FIG. 17 further illustrates the relationships among subscribers usingthe cell-based representation of FIG. 16 according to variousembodiments of the invention.

FIG. 18 is a schematic of one possible embodiment of a hardwarearchitecture according to various embodiments of the invention.

DETAILED DESCRIPTION OF THE INVENTION, INCLUDING THE PREFERREDEMBODIMENT

Referring to FIG. 1, in one embodiment, a reverse-auction system 100 forimplementation into or with a social networking platform (SNP) includesat least one auction server 104, and at least one client 108, 108′,108″, generally 108. As shown, the reverse-auction system 100 includesthree clients 108, 108′, 108″, but this is only for exemplary purposes,and it is intended that there can be any number of clients 108. Theclient 108 is preferably a personal computer (e.g., a PC with an INTELprocessor or an APPLE MACINTOSH) capable of running such operatingsystems as the MICROSOFT WINDOWS family of operating systems fromMicrosoft Corporation of Redmond, Wash., the MACINTOSH operating systemfrom Apple Computer of Cupertino, Calif., and various varieties of Unix,such as SUN SOLARIS from SUN MICROSYSTEMS, and GNU/Linux from RED HAT,INC. of Durham, N.C., ANDROID from GOOGLE INC. of Mountain View, Calif.(and others). The client 108 may also be implemented on such hardware asa smart or dumb terminal, network computer, wireless device, cellular orsmartphone, tablet PC, information appliance, workstation, minicomputer,mainframe computer, or other computing device, that is operated as ageneral purpose computer or a special purpose hardware device used forserving as a client 108 to access and use the SNP and system 100.

Generally, clients 108 are operated and used by consumers to participatein the social network platform such as FACEBOOK, MySpace, and others. Insome implementations, the clients 108 may use other websites andweb-based applications to access the system. In various embodiments, theclient computer 108 includes either a web browser 116, client software120, or both. The web browser 116 allows the client 108 to request a webpage (e.g. from the server 104) with a web page request. An example of aweb page is a data file that includes computer executable orinterpretable information, graphics, sound, text, and/or video, that canbe displayed, executed, played, processed, streamed, and/or stored andthat can contain links, or pointers, to other web pages. In oneembodiment, a user of the client 108 manually requests a web page fromthe server 104. Alternatively, the client 108 automatically makesrequests with the web browser 116. Examples of commercially availableweb browser software 116 are INTERNET EXPLORER, offered by MicrosoftCorporation of Redmond, Wash., and FIREFOX, offered by the MozillaFoundation.

In some embodiments, the client 108 also includes a client softwaremodule 120. In certain instances, the client software 120 providesfunctionality to the client 108 that allows the consumer to participatein an auction with other consumers using the SNP. The client software120 may be implemented in various forms, for example, it may be in theform of a Java applet that is downloaded to the client 108 and runs inconjunction with the web browser 116, or the client software 120 may bein the form of a standalone application, implemented in a multi-platformlanguage such as Java or in native processor executable code. In oneembodiment, if executing on the client 108, the client software 120opens a network connection to the server 104 over the communicationsnetwork 112 and communicates via that connection to the server 104. Theclient software 120 and the web browser 116 may be part of a singleclient-server interface 124; for example, the client software can beimplemented as a “plug-in” to the web browser 116.

A communications network 112 connects the client 108 with the server104. The communication may take place via any media such as standardtelephone lines, LAN or WAN links (e.g., T1, T3, 56 kb, X.25), broadbandconnections (ISDN, Frame Relay, ATM), wireless links, cellular networks,and so on. Preferably, the network 112 can carry TCP/IP protocolcommunications, and HTTP/HTTPS requests made by the web browser 116 andthe connection between the client software 120 and the server 104 can becommunicated over such TCP/IP networks. The type of network is not alimitation, however, and any suitable network may be used. Typicalexamples of networks that can serve as the communications network 112include a wireless or wired Ethernet-based intranet, a local orwide-area network (LAN or WAN), and/or the global communications networkknown as the Internet, which may accommodate many differentcommunications media and protocols.

The auction server 104 may be operated by the same entity operating theSNP, or, in some embodiments, by a third-party application host offeringthe auction service to one or more SNPs. The auction server 104 ispreferably implemented on one or more server class computers that havesufficient memory, data storage, and processing power and that run aserver class operating system (e.g. SUN Solaris, GNU/Linux, MICROSOFTWINDOWS, etc.). Other types of system hardware and software than thatdescribed here may also be used, depending on the capacity of the deviceand the number of consumers using the auction services and the number ofSNPs being supported. For example, the server 104 may be part of aserver farm or server network, which is a logical group of one or moreservers. As another example, multiple servers 104 may be associated orconnected with each other or operating independently, but with shareddata. As is typical in large-scale systems, application softwareimplementing the functions and features described herein may beimplemented in components, with different components running ondifferent server computers, on the same server, or some combination.

Referring to FIG. 2, in one embodiment, the auction server 104 includesa client communication module 206 that is the interface forcommunication with clients 108 and sellers 208. As used herein, sellers208 may be any type of entity, including, but not limited to largeretail chains (e.g., BestBuy, J&R Electronics, RadioShack, etc.),regional or local stores, restaurants, and even individuals. Typically,sellers 208 also communicate with the system using one or more clients108 via the network 112. The client communication module 206 can beimplemented as software running on one or more servers, or may beimplemented as a stand-alone server. In some embodiments, the clientcommunication module 206 can provide an interface both to clientsoftware 120 and to a client web browser 116, so that, for example, aweb browser 116 can be used by a consumer to access their auctioninformation, or to observe the progress of other auctions, and so on,while the client software 120 may be used for participating in anauction. The interface to each of the client software 120 and the clientweb browser 116 can be implemented separately or in combination. Inother embodiments, the client communication module 206 can alsocommunicate using other protocols or mechanisms.

The client communication module 206 communicates with the auctioncontroller 212, which provides the main programming logic for theoperation of the system and executes stored computer instructions usingone or more processors and memory. In one embodiment, the auctioncontroller 212 is implemented as one or more application programsrunning on a server class computer, which may be the same or differentcomputer as the client communication module 206. The auction controller212 receives commands, queries and input from consumers and sellers viathe client communication module 206. The auction controller 212 alsoprovides the functionality for implementing, initiating, managing, andbidding within the social network framework. For example, the auctioncontroller 212 collects information from the users initiating theauctions and finds sellers that are able to provide the products orservices requested by the users in the auction. The auction controller216 determines when an auction should begin or end, and conducts theiterative reverse auction by interacting with the seller-specificauction engines 216, and determines, in real-time, optimal pricing forthe request as bids for the next round. Once a stopping criteria hasbeen met, the auction controller determines a winner (or winners) of theauctions. In some embodiments, the auction controller identifiesadditional consumers that may be interested in joining an auction, aswell performing other functions described in greater detail below.

The server 104 may also include a database server 220, which stores datarelated to the system. For instance, the database server 220 storesauction information, statistics and information about the consumers(such as names, addresses, purchase histories, etc.), product andpricing information, seller information (names, products offered,regions served, etc.) regional information (e.g., country-specificdata), server availability, and web traffic information. The databaseserver 220 provides data to the application server 212. Software offeredby ORACLE Corp., IBM, MICROSOFT, and SYBASE are all examples of databasesoftware systems that may be used to implement the database server 220.In some embodiments, each auction controller 216 includes a local dataserver for storage and processing of seller-specific configurationinformation, pricing data, bidding strategies, sell-specificoptimization parameters, etc.

In some embodiments, the client 108 (FIG. 1) communicates with theserver 104 via a web browser. In such an embodiment, the communicationserver 206 (FIG. 2) also includes a web server. The web server deliversweb pages to the client 108 and provides an interface for communicationsbetween the web browser 116 and the auction server 104. Preferably, theweb server is an enterprise class web server, such as APACHE from theAPACHE FOUNDATION or INTERNET INFORMATION SERVER from MICROSOFTCORPORATION running on one or more server class computers, which may bethe same or different computers than the communication server 206.

For embodiments in which the components and methods are provided as oneor more software programs, the program may be written in any one of anumber of high level languages such as FORTRAN, PASCAL, JAVA, C, C++,C#, BASIC, PERL, various scripting languages, and/or HTML. Data can betransmitted among the various application and storage modules usingclient/server techniques such as ODBC and direct data access, as well asvia web services, XML and AJAX technologies. Additionally, the softwarecan be implemented in an assembly language directed to themicroprocessor resident on a target computer; for example, the softwaremay be implemented in Intel 80×86 assembly language if it is configuredto run on an IBM PC or PC clone. The software may be embodied on anarticle of manufacture including, but not limited to, a floppy disk, ahard disk, an optical disk, a magnetic tape, a PROM, an EPROM, EEPROM,field-programmable gate array, or CD-ROM.

In accordance with various embodiments of the invention, afully-automated live reverse auction based system and techniques foroperating the system are implemented within or operate alongside of anSNP. Unlike conventional largely manual reverse auction platforms (suchas those offered by Ariba), or group-buy sites such as BuyWithMe orGroupOn, the platform and methodology described herein takes advantageof users' (also referred to as “subscribers”) micro-communities to offeropportunities to leverage consumer volume but in a non-intrusivefashion. Further, the automated auction engines allow sellers to competefor the subscribers' business while enabling dynamic pricing, thussimultaneously optimizing its own unique targets such as profit,revenue, traffic patterns and inventory. It provides an on-demand,trusted, transparent, real-time auction in which results are immediatelyavailable to all participants.

Most, if not all, SNPs collect and maintain information about each oftheir subscribers and actively uses such data to serve context andprofile-specific display ads, suggest new connections (e.g., friends orbusiness contacts) and offer other services. While much work as beendone to determine an individual's “profile” for ad targeting, no suchattempts have been made with respect to a subscribers “circle” offriends—or “micro-community.” The systems and techniques describedherein use key characteristics of a subscriber's micro-community onaggregated basis as input into a reverse auction process which allowssellers a unique ability to target a subset of the SNP's subscriberbase. Such characteristics could include but are not limited to medianage, income, education, location, buying patterns as a group, conversionrate, activity level over time, etc. In some cases the profiles (whetherthey be a consumer profile or community profile) may be represented asan index (either a single value or a vector of values), such that theprofile may be mathematically manipulated and used in variouscalculations, as described below.

Referring now to FIG. 3, a subscriber interested in purchasing a productor service (the initiator 305) initiates a transaction by providinginformation to the auction server 104. For example, the initiator 305may be interested in purchasing a 52″ Panasonic TV, and sendsinformation such as the make and model number, desired features, and, insome cases parameters related to price (e.g., a maximum, a minimum, adesired price, etc.) to the auction server 104. The informationsubmitted may also include a desired class of sellers (consumer ratingsof at least 4 stars, a 95% satisfaction rate, etc.), and whether theinitiator 305 is willing to share the details of the transaction withher micro-community 320. Both the initiator's consumer profile and hermicro-community's community profile may be provided to potential sellers315, who may then decide whether to participate in the auction processbased on the deal parameters, the profiles and the seller's own targetparameters. As depicted, only two sellers 315 are shown, however it isunderstood that any number of sellers 315 may receive auctioninformation and participate in the auction process. The sellers 315utilize one or more automated auction engines 216 to determine whetheror not to participate in the auction, whether to continue in an auctiononce initiated, and how to bid during an auction. One exemplaryembodiment of an automated auction engine is the “Seller AutomatedEngine” (SAEJ), as described in U.S. Pat. No. 7,778,882 and co-pendingpatent application Ser. No. 12/716,727, the entire disclosures of whichare hereby incorporated by reference.

Each auction engine 216 evaluates the information provided on behalf ofits seller, according to its configuration, determines whether or not toparticipate in the auction, and if so, participates in the real-timeiterative auction for the product/service requested by the initiator305. The results of each iterative bidding round are provided back tothe engine, thus facilitating an automated, ongoing auction amongmultiple participants without the need for manual intervention. In theexample above, one possible result may include a price that the selleris 315 willing to sell the television for, given certain criteria suchas the number of other possible sales that he may make given the sizeand profile of the initiator's micro-community. Subsequently, theinitiator 305 decides whether to complete the transaction.

To track and monitor the status of auctions as they are initiated,updated, and completed, each subscriber is provided with a unique dealboard. One embodiment of a deal board resembles a “dashboard-like”screen as rendered by the application server and populated with dataregarding deals that the subscriber is either participating in or ismonitoring.

When an auction is initiated, aggregated information about theinitiating subscriber's micro-community is provided to eachparticipating auction engine. The aggregated information, among otherthings, includes the community profile, number of members in thecommunity, etc. This allows each auction engine to compute an optimalprice for its respective seller participating in the auction whilesimultaneously optimizing other target parameters based on thecollection of information. If the auction engine can commence a dealbetween the subscriber and a seller, and the subscriber accepts theoffer and shares the deal with the subscriber's micro-community). Thedeal information is then propagated to the deal boards of each member ofthe micro-community as a secondary item. Thus, each subscriber's dealboard receives notification of time-bound deals from transactions withinhis micro-community. The subscriber can then purchase the item directlyfrom the seller under the same deal parameters as the initial subscriberwas able to secure in her auction process.

As an example, and referring to FIG. 4, subscriber 1's deal board 405includes the transaction she has initiated for item 1—referred to as a“primary transaction.” To take advantage of the SNP infrastructure andinherent trust that exists among the subscriber's micro-community, othersubscribers will also see the transaction on their deal board. Forexample, subscriber 2 may not yet have decided to participate in theauction, and as such the deal for item 1 appears in his deal board 410,but not as a transaction. Subscriber 3, however, has elected toparticipate in the transaction and based on seeing the offer from theseller (and, in some cases, encouragement from other subscribers) and asa result the “deal” is listed in his deal board 415 as a secondarytransaction (i.e., a transaction not initiated by that subscriber). Thedeal boards are updated in real-time (assuming the initiator or thosesubscribers facilitating secondary transactions are willing to share)along with other transactions across one's micro-community. The sharingof information regarding the transactions can be either anonymous (e.g.,someone in your micro-community has initiated a particular deal) orfully disclosed (John Smith initiated an auction—would you like tojoin?). In some cases, the willingness of the initiator to share mayinfluence the bid price and the community profile as well as othervariables as the auction engines compute seller's optimal bids duringauction.

For example, if a subscriber has 100 friends in her micro-community,then her deal board lists deals as they occur in real-time within hermicro-community, assuming the proper consents have been provided. Thesame is true for each of the 100 friends, each of whom has his ownunique deal board, updated based on transaction activity within his ownrespective micro-community.

The deal board may include additional information beyondproduct-specific details, such as: (i) the duration for which the dealwill be available to other members of a micro-community at the sameterms without the need to run another auction (referred to herein as“deal validity duration;” (ii) the number of units available (at thecurrent terms and/or in total); (iii) the actual volume of the dealproviding a context to the price, i.e., the number of units which wererequested by the subscriber to have received the current price (e.g., hepurchased four units to get a 10% discount); (iv) the class of sellerwho sold the goods and various terms and conditions such as its returnpolicy, etc.; (v) the seller's location; and (vi) reputation andcustomer feedback data for the seller.

In some embodiments, the deal board may include corresponding timersshowing the time remaining to participate in and/or complete a secondarytransaction, how many units remain available, and real-time updates asinventory is depleted or other auction parameters change. The deal boardmay also include a short review/experience/comment section. The dealboard may also be integrated as an applet into micro-blogging sites suchas Twitter so a subscriber or seller only needs to provide a singlemessage as a command to the auction site. In some implementations, asubscriber can apply “filters” to her deal board to limit the productsand deals presented to her, the members of her micro-community from whomshe does not want to see deals, or use other characteristics (price,time, etc.) to limit her deal board. Alternatively, she may identifyspecific items which she will always be interested in, and may want tobe highlighted, sorted to the top, and/or receive a message that an itemmeeting a particular criteria has been added to her deal board. Forexample, if a subscriber is routinely interested in deals pertaining totickets to sporting events, she may set an alert that generates a textmessage so she is instantly aware of the deal. Such controls also assistthe sellers to more accurately gauge the demand, as certain subscribers,although eligible to participate in the deal, may indicate that they arenot interested.

Referring to FIG. 5, and continuing with the previously discussedscenario of 52″ Panasonic TV, the winning seller 505 for example maymake the deal available to the subscriber's micro-community (secondarysubscriber 510) for eight hours and set aside twenty units on the sameterms. Thus, the subscriber's micro-community also benefits from thedeal resulting from the subscriber's reverse auction. The duration forwhich this deal is made available to the micro-community on their dealboard 515 and the number of units offered are computed and optimized inreal-time by the seller's auction engine. Among other things, theseparameters may be a function of the micro-community profile, expectedand/or historical conversions for the community, the offer price, andcurrent revenue, profit, and inventory positions compared to theirrespective targets over a pre-defined duration. Members of theinitiator's micro-community then have a window of time available tocomplete a transaction at the same price for up to the number of unitsoffered. Transactions initiated by members of the micro-community arereferred to as “secondary transactions” and each subscriber's uniquedeal board is updated in real-time based on his own deals and dealsreceived by others within his micro-community.

When a secondary transaction is completed, there are multiple ways itcan be managed and communicated through the SNP. For example, thewinning seller may not want secondary transactions to appear in the dealboard of other subscribers within the other subscriber'smicro-community, as the seller may not want to offer the same deal, maynot have sufficient inventory to cover the projected sales, or may havealready met its profit or revenue targets. Alternatively, the seller maybe willing to treat secondary transactions as a new primary transactionat the same price but altering other parameters. For example, the sellermay alter the duration, number of available units, etc. optimizedsubject variables pertaining to the item, the seller and the members ofthe micro-community. In some circumstances, the seller may notice that amicro-community profile indicates that the majority of its members liveoutside the United States and the seller may not be equipped to shipproducts overseas. It should be noted that each secondary transactionmay result in multiple such additional transactions and as a result alarge number of transactions may occur in relatively very short durationdue to the potentially exponential propagation via each micro-communitynode. Thus, a deal can propagate from an initial subscriber node to manynodes very rapidly, as illustrated in FIG. 6.

FIG. 7 illustrates the results of such propagation across deal boards ofa primary initiator's micro-community when a subscriber initiates a “newdeal.” In this instance, Subscriber 1 initiates a first deal whichSubscriber 3 participates in as a member of Subscriber 1'smicro-community. Subscriber 3 then may initiate a transaction thatpropagates to his micro-community (Subscriber M, M+1 . . . N).

In addition either allowing a seller to specify the offer duration orcalculating the auction offer validity duration empirically, theduration may also be computed analytically using a time dependentbid-response function ρ(t,p,d,i). In addition to depending on time t,the function also depends on price p, offer duration d and a communityindex i.

The expected sales volume from the deal board may then be representedas:

$\begin{matrix}{{\Delta\; N} = {N{\int_{0}^{d}{{\rho\left( {t,p,d,i} \right)}d\; t}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$where N is the number of members in the initiating subscriber'smicro-community.

The expected revenue is then calculated as:ΔR=ΔNp   Equation 2and the expected profit is:ΔM=ΔN(p−c)+f(N,p,d,T)   Equation 3where c is the unit product cost, and f is inventory related costreduction, which is a function of volume N, price p, duration d andremaining target time T. Possible inventory costs include item cost,carrying cost, ordering cost and stock-out cost.

The total expected contribution to the seller's utility may then beformulated as:Δu=w _(N) u _(N)(N _(T) ,N _(t) +ΔN)+w _(R) u _(R)(R _(T) ,R _(t) +ΔR)+w_(M) u _(M)(M _(T) ,M _(t) +ΔM)   Equation 4where:

-   -   w_(N)=sales volume utility weight;    -   u_(N)=sales volume utility function;    -   N_(T)=sales volume target;    -   N_(t)=real time achieved sales volume across multiple        deal/volume boards before the auction;    -   w_(R)=revenue utility weight;    -   u_(R)=revenue utility function;    -   R_(T)=revenue target;    -   R_(t)=real time achieved revenue across multiple deal/volume        boards before the auction;    -   w_(M)=profit utility weight;    -   u_(M)=profit utility function;    -   M_(T)=profit target; and    -   M_(t)=real time achieved profit across multiple deal/volume        boards before the auction.

The optimal bid price and offer duration is then determined as:

$\begin{matrix}{\arg\;{\max\limits_{p,d}{\left( {\Delta\; u} \right).}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

The expected sales volume ΔN can be used as number of units to offer forthe auction. Additional constraints can be applied to the optimization,such as limited inventory, supplier's break and so on.

In cases in which it is difficult to estimate multidimensionalbid-response functions based on historical data, and alternativeapproach removes the time dimension from the optimization and theoptimization process is limited to price. In such cases, the offerduration is limited to a short period of time, and the number of unitsoffered is set conservatively low based on current inventory level. Atthe end of the offer duration, or after the desired number of units havebeen sold, a decision is made whether to extend the offer duration orreset the number of units available. The decision may be based oncertain real-time data such as inventory levels, number or dollar amountof pending transactions, conversion rates and/or newly added deal/volumeboards for the same product. If not extended, the deal expires.

Using this approach, there is no need to estimate time basedbid-response functions or to calculate an optimal offer duration ornumber of units to offer. As a result, inventory risks are reduced andprofit is improved by making decisions in real time.

In certain implementations, the auction engines may cause secondarytransactions to appear in the respective deal board of a subscriberhowever, instead of offering separate number of units for eachmicro-community, may offer a common pool across all deal boards. As anexample, a seller may make 150 TVs available with same or differentexpiration timer associated with each transaction. Every time anadditional TV is purchased by a subscriber (regardless of whichmicro-community to which they belong), the inventory available for allmembers is reduced and that subscriber's deal board is also updated withthe remaining shared inventory. The status of inventory issimultaneously updated correspondingly at all the related deal boards inthis example.

Such an approach allows sellers to very quickly sell a large number ofunits in a fairly short time at a significantly reduced, predictable andmeasurable acquisition cost when compared to conventional cost-per-clickmodels using key attributes of the social networking platform. As aperspective on the magnitude, if one subscriber has 100 friends in hermicro-community, and each of her friends has 200 friends in their ownrespective micro-communities, each of whom has 300 friends in theirrespective micro-communities, assuming each friend is unique in thismodel, there is a potential of reaching 6 million (100×200×300)subscribers in a very short order.

Each automated auction engine thus examines in real-time the deals ithas won, their expected conversion rate, expiry time left, and potentialimpact on overall inventory, profit, revenue and other targets prior toparticipating in next auction or making a new bid.

For a given transaction, the number of deal boards displaying thetransaction may grow as new deal boards are added. The deal validityduration and the number of units for each board may be determined by thewinning seller's auction engine, based on the associated communityprofile of the new deal board and the performance of other deal boardsindividually. Other components used to determine deal board parametersmay include an aggregated amount of deal validity duration left acrossall boards, cumulative conversion rates, variance from an expectedconversion as a function of time, and the number of units sold. Using aretail implementation as an example, the real-time feedback from each ofthe existing deal boards coupled with the difference between theexisting inventory, revenue, supplier's break, profit, and otherparameters as compared to individual and aggregated deal board targetsmay used to determine the deal validity duration and the number of unitsto be offered for a new deal board. Thus, the number of feedback inputsto this seller-automated engine increases as progressively more dealboards are added and influence the outcome for the next deal board. Thisreal-time feedback engine thus has two levels of optimizations fortransactions related to this particular product. One for the deal boardbased on its unique parameters and another which is across all the dealboards associated with this specific transaction, product or servicebeing offered.

Another independently initiated transaction by the same auction enginefor the same product in an overlapping time frame may be assigned to aunique deal board, and the feedback loops associated with each dealboard for the same product may be evaluated individually and/or jointlyin real-time to compute the parameters values for the new deal board.

Each auction engine thus has associated therewith a common inventory andproduct-specific supplier's break-point for each product, thus providinga third level of real-time optimization that occurs in conjunction with,and simultaneous to, the two optimizations outlined earlier for a givenproduct across independently-initiated transactions occurring in anoverlapping time frame.

Referring to FIG. 7A, these same three optimization functions may beused across deal boards offering different products. While the inventorynumbers may be different for each product, the seller may have revenueand profit targets across the products, for example at the corporatelevel. These constraints may provide for a fourth level of real-timeoptimized computations using the feedback from each of the abovesub-systems for same or different products in conjunction with andsimultaneous to the previous three outlined above. All these feedbackinputs may be used in the computation to determine the parameters forthe next deal board. In each case, a seller's supplier's break may beproduct specific, calculated across product lines (vendor specific), orboth. Thus the number of feedback paths into this closed loop adaptiveengine can grow rapidly.

Each auction engine continuously monitors the status and performance ofeach of its deal boards and depending on the performance of a dealboard, will extend the deal validity if, for example, additionalinventory is added or the profits continue to meet or exceedexpectations. In one of the alternatives, the incremental duration mayhave certain constraints such as it can only be increased once, or itcan be only 50% of the original duration. In some instances, some dealboards may be extended any number of times and for any length of time.

If the subscriber is not willing to share the deal on the deal boardwith her friends, the auction is simply run as a single unit auction (ormultiple units being purchased by a single subscriber). However, if thesubscriber is willing to share the deal with her friends, the auction ismodeled as a multi-unit auction, allowing the seller to sell more tothis community with lower prices.

Example

For any given subscriber's micro-community with N members, the expectedsales volume from the community is:ΔN=Nρ   Equation 6where ρ is a time-aggregated bid-response function within the auctionduration, which is a combination of a seller's winning probabilityagainst other sellers and acceptance probability by the members of thismicro-community. The bid-response function can be considered as thefinal conversion ratio, and is also a function of bid price p andcommunity profile i.

The expected revenue from the micro-community is thenΔR=Nρp   Equation 7and the expected profit is calculated asΔM=Nρ(p−c)   Equation 8where c is the unit cost.

As described earlier, the computation of bids for an auction isinfluenced by the presence of a non-zero deal validity duration dealboard/s for an item.

In some cases, there may be multiple active deal boards for the sameproduct, each initiated from within different (in some cases overlappingand in some cases non-overlapping) micro-communities. For example,suppose there are K active deal boards for the same product. The numberof members of each micro-community for each board is N_(i) (where i=1,2, . . . , K), and the product price is P_(i) (where i=1, 2, . . . , K).The conversion ratios for the remaining deal durations are ρ₁, ρ₂, . . ., ρ_(K). To find the optimal price p for a newly added deal board with Nmembers and bid-response function ρ for the same product, the expectedsales volume for the product from each of the micro-communities iscalculated using the following formula:

$\begin{matrix}{{\Delta\; N} = {{N\;\rho} + {\sum\limits_{i = 1}^{K}{N_{i}\rho_{i}}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

The expected revenue for the product from the all of themicro-communities is then calculated as:

$\begin{matrix}{{\Delta\; R} = {{N\;\rho\; p} + {\sum\limits_{i = 1}^{K}{N_{i}\rho_{i}P_{i}}}}} & {{Equation}\mspace{14mu} 10}\end{matrix}$with an expected profit of:

$\begin{matrix}{{\Delta\; M} = {{N\;{\rho\left( {p - c} \right)}} + {\sum\limits_{i = 1}^{K}{N_{i}{\rho_{i}\left( {P_{i} - c} \right)}}}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$where c is the unit product cost.

The total expected contribution to the seller's utility can then beformulated as:Δu=w _(N) u _(N)(N _(T) ,N _(t) +ΔN)+w _(R) u _(R)(R _(T) ,R _(t) +ΔR)+w_(M) u _(M)(M _(T) ,M _(t) +ΔM)   Equation 12where:

-   -   w_(N)=sales volume utility weight;    -   u_(N)=sales volume utility function;    -   N_(T)=sales volume target;    -   N_(t)=achieved sales volume before the auction;    -   w_(R)=revenue utility weight;    -   u_(R)=revenue utility function;    -   R_(T)=revenue target;    -   R_(t)=achieved revenue before the auction;    -   w_(M)=profit utility weight;    -   u_(m)=profit utility function;    -   M_(T)=profit target; and    -   M_(t)=achieved profit before the auction.

The optimal bid price is determined as:

$\begin{matrix}{\arg\;{\max\limits_{p}{\left( {\Delta\; u} \right).}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$and additional constraints can be applied to the optimization, such aslimited inventory, supplier's break, etc.

The deal board validity duration is determined empirically. Assumingthat the seller desires to capture the potential additional deals at thesame price, the validity duration may be calculated as:

$\begin{matrix}{\frac{\int_{0}^{T}{{\rho_{t}(t)}d\; t}}{\int_{0}^{\infty}{{\rho_{t}(t)}d\; t}} = \alpha} & {{Equation}\mspace{14mu} 14}\end{matrix}$where:

-   -   ρ_(t)=estimated acceptance ratio as a function of time;    -   T=auction duration; and    -   α=pre-determined, aggregated acceptance proportion.

The validity duration may also constrained by other factors, includingavailable inventory levels of the seller, a cut-off date (e.g., end ofmonth, quarter, etc.); a supplier's break, as well as certainpsychological impacts where the validity duration impacts buyerbehavior, which in turn affects the acceptance ratio, which can bemodeled and factored into the bid response if desired.

In addition to deal boards, which list currently-executing deals as theyevolve and expand to additional subscribers and sellers update theirbids, the system also provides a dashboard of “proposed” deals using a“volume board.” Continuing with the example of the subscriber interestedin buying a 52″ Panasonic TV, instead of the subscriber initiating anauction right away and consummating it when a seller wins, she mayinstead initiate a request on her volume board, effectively creating a“pool” of highly-interested potential buyers. This pool becomes valuableto sellers as the members are likely motivated to commit to atransaction, especially at certain price points. The request includesvarious deal parameters, including but not necessarily limited to theproduct and related details, a number of units, a desired delivery date,and the date/time she intends to specify as the start of the finalauction (e.g., in one week); acceptable class(es) of sellers, a minimumvolume needed to start the auction, results of interim auctions to showa reference/commitment price for a single unit by the seller (for thedesired volume of the product), and a commitment price at which everyonewho agrees to join the pool is committed complete the transaction. As anexample, at any point within a time duration of seven days from startprior to the final auction, if the price offered by a seller were toreach $400 then all subscribers in the pool are committed to stay in thepool and cannot withdraw. In some instances, the sellers may be madeaware of one or more of these parameters (likely the product details anda price), but not others. In some instances, each subscriber may be ableto alter some of the parameters (e.g., delivery date) which may or maynot be shared with the sellers.

The act of requesting a product by the primary requestor is referred toas “initiating” a request for the product. Once initiated, each specificinstance of the request for the product is referred to as an “initiateditem” or simply an “item.” A secondary requester is referred to as“joiner” and the act of requesting in this form is also referred to as“joining” the item.

If a member of a subscriber's micro-community either initiates or joinsan item, that community member becomes “enabled” to join the item aswell. In some instances, a subscriber may be enabled for the same itemby more than one member of their micro-community based on nodalconnectivity among the SNP subscribers. In this case, the subscriber maychoose any one of the possible enablers and join the item, and thesubscriber offering that item is referred to as the “selected enabler.”

A given product (identified by SKU, model number, etc.) may be initiatedmultiple times by multiple subscribers, and, in some cases, a singlesubscriber may also be allowed to initiate the same product multipletimes with different price parameters. When an item is initiated in theinitiator's volume board, it appears in his micro-community members'volume boards, and when a micro-community member joins the item, theitem appears on all of his micro-community members' volume boards. Forexample if each of the initiator's micro-community members have anaverage of 100 members in their respective micro-communities, then eachjoin requires an average of 100 volume boards to be updated. If 10% ofthe initiator's micro-community members join the item, and 10% of theirrespective micro-community members join the item, and so on for fourdegrees of separation from the initiator, the total number subscriberswho have joined the item (including the initiator) is1+10+100+1,000+10,000=11,111. The number of subscribers either joined orallowed to join the item is1+100+(10*100)+(100*100)+(1,000*100)+(10,000*100)=1,111,101. As aresult, any changes to a single item initiated by a subscriber will bereflected in upwards of 1 million volume boards in real-time. Combinedwith the millions of items being initiated by millions of subscriberswho may have hundreds of micro-community members, the synchronizationand accuracy of all the volume boards becomes increasingly critical.

A subscriber may “leave” (or “unjoin”) an item from his volume board aslong as his commitment price (or the common commitment price) has notbeen met. In such a scenario, the item may be removed from hismicro-community members' volume boards, except in cases where thesubscribers might join the item through other enablers. For thosemicro-community members who joined the item through a particularsubscriber (the selected enabler), they may unjoin the item, or selectanother enabler (if available to them) to re-join the item. As a result,the act of unjoining can ripple through the SNP in an exponentialfashion. In the worst case, if the initiator “leaves” then the entirechain is unraveled and all the subscribers are removed from the item. Inthe above example, where the average number of micro-community membersis 100, and an item has been accepted by 10% of subscribers in each offour degrees of separation, the item will be removed from all 1,111,101volume boards. Alternatively, the initiator may transfer hisresponsibility to someone in their micro-community to continue on as theprimary initiator. Moreover, because many subscribers have overlappingmicro-communities (i.e., common friends among multiple subscribers) asubscriber may have multiple enablers for the same item. Further, thesystem may prevent the appearance of an item in an enabler's volumeboard based on another subscriber's joining the item if that subscriberis connected to the enabler though other subscribers, effectivelypreventing loops.

One parameter determined by the initiator and placed on the volume boardis a “dream price,” defined as the price at which the initiator and allof those who join the buying pool are committed to buy regardless of thevolume and without requiring a final auction. Using the example abovefor the time duration of seven days, if the price offered by a seller(based, for example, on an interim auction and/or particular sellertargets) were to reach $375 at anytime within the seven days prior tothe final auction, then the transaction can be immediately consummated.In some cases, there may be certain participation criteria set (eitherby the initiator or a system administrator, or both) that must be metfor a subscriber to join the buying pool. For example, if an initiatoris looking to pool a group of subscribers to negotiate a large block ofauto loans, he may specify a criteria such as a minimum credit score of720, the loan size be greater than $10,000, the loan be no more than acertain percentage of the value of the car, and so on.

After placement of the request on the volume board, the volume boardsassociated with the subscribers that are members of the initiator'smicro-community are updated in real-time and each subscriber can decideif she is interested in pooling with the request. In certainimplementations, a subscriber's volume board may reflect multipleinitiating requests for different products pending at any point of timeand from different members of their micro-community. Thus, eachsubscriber's volume board is unique, and provides an opportunity to thesubscriber's micro-community to benefit from a volume transaction. Thisvolume board may also include both unit and volume pricing from recentauctions to show the potential and/or expected benefits to thesubscribers.

In this real-time volume board, the subscriber initiating the request isconsidered the “primary requester” and her friends in hermicro-community who decide to join the pool or anyone thereafter (whoalso meets the specified participation criteria, if any) are considered“secondary requesters.” Consider again the example of a primaryrequester looking to buy a 52″ Panasonic TV who initiates a request topool. Subsequently, when one of her friends decides to join the pool forthe TV, the volume board of the primary requester, each subscriber inher micro-community, and subscribers in the micro-community of thesecondary requester are updated with a volume of two—meaning there arenow two subscribers that are willing to be part of this deal. Thus, theoriginal request propagates from a single node to many nodes assecondary requesters join the pool. Each secondary node has at least twolinks to trusted friends in its own micro-community (unless it is thelast node with no follow-on connectivity), one from the subscriber fromwhich she received her updated volume board and one from the subscriberwho followed. This node-to-node relationship has a major advantage overa common pool that anyone can join independent of relationships amongthe participants, because common pools are subject to manipulation bysellers and often complied by entities other than the subscribersthemselves, and the terms of the offers are typically determined inadvance by the sellers. In contrast, the node-to-node relationshipstructure is defined solely by the personal and professional connectionsof the subscribers, and the proliferation of the deals are based onsubscriber-provided deal parameters.

As the volume of subscribers increases, interim auctions areautomatically run, time-stamped, and propagated to the volume boards ofeach requester and the members of their micro-communities. Some of theseinterim auctions may be classified as “price progress” auctions. Forexample, the winning auction for a TV by the initiator may haveoriginally been $500, but the same TV may be priced at $475 for a highervolume (e.g., five). This updated, improved pricing for increased volumemay entice others to join and another auction may then commence. Thisprocess can quickly spread across many micro-communities resulting inexponential volume growth. This process capitalizes on the keyattributes and building blocks of the SNP platform and in effect formstransient and virtual buying pools consisting of friends who trust eachother for the purpose of initiating, viewing and/or completingtransactions for virtually any product/service, and once the transactionis completed the pools automatically disjoin.

In some implementations, the number of subscribers whose commitmentprice has been reached may also be used as input into the automatedauction engines working on behalf of the prospective sellers. As notedabove, if the buyers “dream price” has been reached, the deal isautomatically consummated, as no auctions are needed and the firstautomated auction engine securing the right to sell at this price isautomatically declared the winner. In cases in which multiple auctionengines offer the dream price, ties may be broken randomly or using asystem-defined policy. However, if the dream price is not reached by theend date specified by the initiator (the date of the “final auction”) orno dream price is specified, the final auction is executed. In someembodiments, the final auction may be run using a “bump-up factor” (BUF)process as described in U.S. Pat. No. 8,522,270 (application Ser. No.11/974,808), the entire disclosure of which is incorporated herein byreference. The BUF process eliminates certain limitations and lastminute manipulations of a time-constrained auction as exemplified by theso-called “sniping” of auctions on eBay, and instead optimizes theobjectives of both the buyer and the seller at the same time.

Each item is uniquely identifiable in the system, i.e., when an item isinitiated, it is assigned a unique global id, referred to as “item id”or “item identifier.” An item database stores item information such asitem id, product id, price, winning seller id and other informationneeded to maintain the state of the item across all active deal boards.As the number of items grow, the item database may be a collection ofmultiple physical databases holding subset of items, thus providing ascalable solution.

FIGS. 8 and 9 illustrate examples of a deal board and volume board,respectively as they appear to subscribers using the SNP to initiate andparticipate in various auctions. For clarification, the deal boarddepicted in FIG. 8 provides information about completed transactionsamong a subscribers micro-community which may be available to them onthe same or similar terms, the volume board of FIG. 9 providesinformation about pending transactions in which subscribers join a groupthat will eventually complete the transaction together. In some cases,transactions completed through a volume board may be propagated to dealboards of other subscribers to the SNP based on their relationships,characteristics, search results, etc.

More specifically, FIG. 8 illustrates a deal board 800 for an individualsubscriber that includes primary deals 805 (deals initiated by thatsubscriber) and secondary deals 810 (deals she did not initiate but isenabled to participate in or may already be participating in). For eachdeal, the deal board 800 lists the item being sold, which may beindicated using an item id 815 and/or an item description 820. The dealboard 800 may also list the deal validity duration 825, the number ofunits available 830 and, in some cases, may also provide a text field835 in which a deal initiator may enter reviews and/or commentsregarding the item being sold, the deal, the subscriber herself, as wellas comments. For secondary deals 810, the deal board may also indicatethe ID, name, nickname or other moniker for the subscriber thatinitiated the deal, so members of their micro-community will know whichone of their friends or contacts initiated the deal.

FIG. 9 illustrates one possible embodiment of a volume board 900 thatincludes initiated deals 905, joined deals 910 and deals that areavailable to join 915. Similar to the deal board, the volume board 900also includes an item id 920 and/or description 925 to identify thesubject of the deal. A deal duration 930 may be included, as well as thedream price 935 and commitment price 940 for the deal. For joined dealsand deals available to join, the initiating subscriber ID and/or name945 may be included. In other instances, additional deal parameters 950may also be included on the volume board 900.

Each subscriber has a unique volume board, that contains a list of items(in the form of item ids and/or descriptions), categorized in one ofthree categories—Initiated items, Joined items, and AvailableToJoinitems. Items in the Joined category include the enablers' ids(subscribers offering the deal) as well as the selected enabler's id(the subscriber who initiated the joined deal). Items in AvailableToJoincategory include a list of enablers' ids. In order to prevent loops inthe system, a given item appears once in a subscriber's volume board.If, for example, an item to be added to a subscriber's AvailableToJoinlist already exists, then any new enablers ids are appended to theitem's enabler's list. To remove an item from the AvailableToJoin list,every enabler must either have left or unjoined that item. If at leastone enabler still remains, the item stays in the AvailableToJoin listand those enabler's that leave or unjoin are removed, but the itemremains listed with the other enabler ids.

FIGS. 10-16 illustrate the events and actions that create and maintainthe items on the volume boards across the SNP. For an “initiate” event,(FIG. 10), a item is created in the item database and the item volume isset to 1 (step 1005). The item is added to the “initiated items” listfor that subscriber (step 1010), and propagated to the “available tojoin” lists for each member of the subscriber's micro-community.

FIG. 11 illustrates the join process, which represents a subscriberjoining into a deal initiated by another subscriber and viewed on avolume board. When a subscriber joins the deal, the item volume isincremented up by one (step 1105). The subscribers volume board is thenupdated by removing the item from the AvailableToJoin list, and addingit to the Joined list, along with the enabling subscriber's id (step1110). A check is then made to determine if the item is already in theAvailableToJoin list of the joining subscriber's micro-community (step1115). If it is, no further action is taken. If not, the volume boardsof the joining subscriber's micro-community are updated with the newitem (step 1120).

FIG. 12 illustrates the process by which a subscriber who had initiateda deal effectively cancels the deal by removing it from the system. Thesubscriber first selects and then removes the item from her list ofinitiated items (step 1205). The item is then removed from the Joinlists of the members of the initiating subscriber's micro-community whohad joined the deal, and from the AvailableToJoin lists of all othermembers of the initiating subscriber's micro-community (step 1210). Theitem is then removed from the item database (step 1215). In someinstances, the initiating subscriber cannot remove the deal once acommitment price has been met (e.g., at least one seller is willing tosell the item at the price requested by the initiator).

FIG. 13 illustrates the process by which a subscriber “unjoins” orleaves a deal. The subscriber selects the deal from her Joined list, andthe item count is decremented by one (step 1305). The item is removedfrom her Joined list and placed back into her AvailableToJoin list (step1310). The item is then removed from both the Joined list (for thosesubscribers that joined the deal) and AvailableToJoin list (for thosesubscribers that had not yet joined the deal) across the subscriber'smicro-community (step 1315). If the subscriber wishes to rejoin, theprocess illustrated by FIG. 11 above is repeated.

FIG. 14 illustrates the steps performed when a subscriber connects toanother subscriber (the newly connected subscriber), thus becoming a newmember of her micro-community. A check is made to determine if the itemsalready exist on the newly connected subscriber's Join list orAvailableToJoin list based on a connection through another subscriber(step 1405). If they do not, the Initiated items and Joined items fromthe initial subscriber are added to the newly connected subscriber'sAvailableToJoin list (step 1410). Further, a check is made to determineif the items listed in the Initiated items list and Joined items listfor the newly connected subscriber are listed in the AvailableToJoinlists of the subscribers she is now connected to (step 1415). If not,the items are added to the AvailableToJoin lists of those subscribersnow connected to the newly connected subscriber (step 1420).

FIG. 15 illustrates the steps performed when a subscriber (thedisconnecting subscriber) “disconnects” from another subscriber. Itemsinitiated by the disconnecting subscriber, whether joined or not, areremoved from either the Joined list (if the subscriber joined the deal)or the AvailableToJoin list (if not) (step 1505). The same process isexecuted in reverse (step 1510), removing items initiated by or joinedby the non-disconnecting subscriber form the Joined or AvailableToJoinlist of the disconnecting subscriber. Further, any items that wereremoved from the Joined list of the non-disconnecting subscriber arealso removed from the AvailableToJoin lists of members of hermicro-community (step 1515). Similarly, when a subscriber removersherself from the SNP entirely (i.e., unsubscribes or unregisters), anyitems initiated by that subscriber are removed from the item database,and therefore are removed from any volume boards of the members of hermicro-community.

Each time a subscriber's volume board is updated, a message (either anemail or a message from within the SNP) is sent to the members of hermicro-community, and their volume boards are automatically updated basedon the steps described above. Those updates are also propagated to thenext set of micro-community members, and so on. This fully-distributedapproach allows each volume board to be independently updated and doesnot require (but, in some cases may use) a central store for the dataused to populate and manage the volume boards. The independence of thevolume boards, along with the update mechanism described above alsoprevents race conditions and deadlocks. For example, a particularsubscriber's volume board may receive messages from multiplemicro-community members. The messages can be processed sequentially andresulting changes applied to the specific volume board independent ofthe activities on the other volume boards. The independence of thevolume board also lends itself to a highly distributed architecture, asdescribed below.

In some implementations, subscribers may be able to extend their view ofdeals beyond their own micro-community. In such cases, a subscriber mayselect a “degree of separation” from herself for which she can seedeals. For example, a subscriber may decide that she wants to see dealsthat are two degrees of separation from her—meaning she will be able tosee and join deals initiated by members of her micro-community, as wellas members of their micro-communities. Such a parameter may be preset insome cases or user configurable. Other possible system-level parametersmay include:

-   -   a limit on the number of micro-communities any particular volume        deal can traverse;    -   a limit on the absolute number of subscribers that can join any        (or a particular) pool;    -   whether or not sellers can (or must) fill all of or only a        partial volume of the deal;    -   whether sellers can “pool” their inventory, thus allowing        multiple sellers to split the volume;    -   whether secondary requestors may quit a deal until a pre-defined        time frame prior to the final auction assuming the commitment        price has not already reached or whether once joined a pool may        not be quit; and    -   whether the deal initiator can specify certain participation        parameters, thus limiting who can join.

Each seller's auction engine performs two real-time functions related tothe volume board. First, each auction engine determines whether toparticipate as an active bidder in numerous volume board-relatedauctions on behalf of a potential seller, and if so, enters the auction.Second, the auction engine scans the dream price (assuming it isprovided to the sellers) of each deal and determines which deals toaccept without initiating an auction.

The auction engines use numerous inputs when decided whether toparticipate in an auction and, if participating how to compute new bidvalues. For example, the auction engines may consider the potentialvolume of the deal, the current estimated revenue and/or profit to bereceived if the deal is consummated at the current price, inventorylevels, a common commitment price (and how many subscribers havecommitted at that price), community profile information (eitherindividually or aggregated) and supplier's break (discounts given to theseller by the supplier of sales reaches a certain threshold during aspecific time period) as compared to the targets over a pre-definedduration such as a day, week, month or a quarter. Or, in someimplementations in which each secondary requester can provide their owncommitment price, the auction engine may consider the difference betweenthe total volume and the number of subscribers whose commitment pricehas already been reached, essentially deriving the number of subscriberswho are still looking for a better price. When scanning dream prices,the auction engine periodically computes gain in real-time acrossmicro-communities for the products of interest and correspondinglyoptimize the targets. For example, the auction engines can decide tooffer the product at the dream price if it expects sufficient marginalsales such that the supplier's break will come into effect.

For large SNPs, there may be hundreds or even thousands of initiators atany one time, and, as a result, hundreds of thousands of volume boardsfor a high-demand product. This multi-tiered, simultaneous optimizationof various seller's objectives may be addressed using two levels ofreal-time processing for computations and optimization related to aparticular product. The first level optimizes a particular product pricein each initiator node and all of its secondary requesters, and thesecond level looks across all initiators and their respective secondarynodes for the same product.

When a subscriber successfully consummates a deal as the primaryrequester via volume board, that same request may be placed on thesubscriber's deal board, along with a duration for which the same (orsimilar) deal will be made available to others in her micro-communityand/or the inventory available for that product. In some cases, a sellermay restrict the deal to the primary requestor's micro-community,whereas in other cases, the seller may allow the deal to expand to asecondary requestor's micro-community. The seller may offer the sameduration for each buyer, or may offer different durations subject to theseller's inventory status, the community profiles for each of thebuyers, predicted buyer conversion rates, the order the pool was created(e.g., FIFO, LIFO, etc.) other pending deals, as well as other factors.

Each auction engine computes auction bids in response to pooled requestsfor specific products. The bid calculation process considers thetransaction volume, the number of subscribers who have met a commitmentprice, any specified dream prices, and other participation criteria suchas expected conversion rates and community indices of some or all of thesubscribers who have joined the pool. In addition, the auction enginesmay consider the performance of some or all non-zero deal validityduration deal boards for the same product at that moment in time. Forexample, if the results from these deal boards (either individually orin the aggregate) indicates a better than expected performance based onseller's desired targets (e.g., revenue profit, inventory, etc.) overtime, the engine may be less aggressive with its bids. On the otherhand, if the performance is less than expected, the auction engine maysubmit more aggressive bids across multiple requests, each within theseller's constraints. Thus, this feedback-driven adaptive closed-loopengine may produce different optimized bid outcomes for two independentauctions with corresponding pool requests which have similarcharacteristics in terms of volume and number of subscribers who havemet the commitment price. In contrast to conventional rule-basedengines, this approach uses real-time feedback and actual progress,status and/or results of ongoing auctions to influence later stages ofindividual actions. This can result in very different outcomes, even forauctions having similar or identical volume board characteristics.

In some implementations, a subscriber may search for a specific itembeing pooled on a volume board across the SNP. Such a feature is veryuseful when a subscriber is interested in a product that is expensive,rare and/or traded in relatively lower volumes because such items may bedifficult to pool to a meaningful size in a relatively short duration.For example, a 103″ Panasonic plasma TV typically may sell for $70,000and at very low volumes (often only one). If the search results show anoutstanding pool request for the item, the subscriber may join thevolume board even through the request did not originate from within hermicro-community. The SNP may, however, constrain subscribers fromfurther propagating within this micro-community. Alternatively, the SNPmay not constrain the subscriber, and treat the request as the joiningof two separate pool requests. As such, instead of initiating anotherindependent request to create a pool, the subscriber has chosen to joinan existing volume board in conformance with its commitment price anddream price constraints, if any. In yet another alternative embodiment,two such separate independent pool requests may be joined at the requestof one of the primary requesters, however, the nodal propagation of eachpool request may remain independent (e.g., no cross-community expansionsuch that a subscriber can only join one volume board for a particularproduct) while combining the overall deal volume. The volume board mayalso display two distinct numbers, those who are part of a nodalpropagation, and those who are not. The joining an unrelated existingpool may be automated by combining the volume of multiple pools forsellers to bid on the overall volume, while volume information for eachinitiator is preserved so that each trusted micro-community only seesits own volume. As an alternative, initiators may provide configurationoptions to make their volume board visible via search (or in a listing)across the social network, and allow initiators (for the same item) fromoutside its micro-community to join the volume board. When a new requestfor the same item is initiated, the SNP can automatically search to seeif this item already exists in another volume board, and recommend thatthe new initiator join the existing volume board.

Similar functions may be performed by the same auction engine for otherrequests for the same product in an overlapping time frame. However,because the product in both transactions is same, it is likely that itshares a common inventory pool and possibly a product-specificsupplier's break. In such instances, additional real-time optimizationcomputations are performed prior to bid in an attempt to optimize thebid for the product across various volume boards, in addition to eachinitiated request. Thus, anytime a subscriber joins a volume board ofinterest to a seller, it may impact bid computations for each such newrequest across all the products of interest, as well as bid computationsfor dissimilar products.

One possible variance of the volume board that supports low-priced,high-volume products is referred to herein as a Mega Volume Board or“MVB”. An MVB may be useful in situations such as buying a popular song,downloading a popular movie, or buying a video game. Whereas thesubscriber may use the standard volume board and wait for volume tobuild-up via the nodal propagation, MVBs have short durations and highvolumes to allow for items the subscribers want to purchase immediately.In such cases, the subscriber may search the SNP for and join an MVB forthe desired item. For example, a song selling for 99 cents may be pooledfor only a 15 minute duration, but may receive 10,000 requests. Thisallows subscribers to purchase products almost instantly, but still getthe benefit of the group auction model.

In some cases, a subscriber may provide a list of items and allow thesystem to search for all of the items automatically instead of having toperform multiple manual searches. Once an MVB is found, the system mayjoin the MVB (or initiate one if none is found) for each item. The listmay include an type of item, however items available for instantdownload (e.g., songs, moves, books, software, etc.) are especiallysuited for the MVB model. This system substantially simplifies thesubscribers' access to and usability of the system by eliminating manyindividual transactions and providing the benefits of pooling whileoffering a unique solution for a class of high-demand and typicallylow-priced products. This same approach may also be used for otherapplications, such as popular gift cards (e.g., Starbucks, Borders), inwhich the face values of the cards can be pooled, but the subscriberreceives additional value (extra credits, a gift, points, etc.) beyondthe face value of the card.

Another benefit of the MVB is that it reduces the credit cardtransaction fees and other payment charges (e.g., PayPal) which form ahigh percentage of a low-priced item. By pooling the requests, the SNPmay become the intermediary and cover payment of these fees and, in somecases, share the savings with the subscribers.

Because the transaction volume for MVBs is significantly higher thanstandard volume boards, progress auctions may be held at much higherthresholds. For example, auctions may occur for every one thousand newsubscribers that join the pool, on a periodic basis (e.g., every 5minutes), or a combination of both volume and time. For example, if10,000 requests are received in a minute, an auction can be held everyminute, on the other hand if 2000 requests are received every minute,the progressive auctions may be held every five minutes and so on.

In another embodiment, a few friends can pre-form a group (e.g., workcolleagues who eat lunch together every Friday) and invite restaurantsto bid for their business by offering discounts. The auction can be heldat a pre-determined time, or each restaurant can make an offer by apre-determined time frame, or make offers any time prior to the Fridaylunch. Everyone in the group does not need to join to take advantage ofthe offer, rather the pricing may be tiered based on the number of folksin the group who take advantage of the offering. In yet anotherembodiment in which the group wishes to attend a particular restaurant,the reverse auction process is eliminated, and instead the singleseller's pricing or discount decision are based on real-timeoptimization of its targets as the number of people accepting the offerand related parameters evolve.

The volume board auction offer duration may also be computedanalytically using a time dependent bid-response function ρ(t,p,d,i). Inaddition to depending on time t, the function also depends on price p,offer duration d and a community index i.

The expected sales volume from the volume board may then be representedas:

$\begin{matrix}{{\Delta\; N} = {N{\int_{0}^{d}{{\rho\left( {t,p,d,i} \right)}d\; t}}}} & {{Equation}\mspace{14mu} 15}\end{matrix}$where N is volume board requested volume.

The expected sales volume is then calculated as:ΔR=ΔNp   Equation 16and the expected profit is:ΔM=ΔN(p−c)+f(N,p,d,T)   Equation 17where c is the unit product cost, and f is inventory related costreduction, which is a function of volume N, price p, duration d andremaining target time T. Possible inventory costs include item cost,carrying cost, ordering cost and stock-out cost.

The total expected contribution to the seller's utility may then beformulated as:Δu=w _(N) u _(N)(N _(T) ,N _(t) +ΔN)+w _(R) u _(R)(R _(T) ,R _(t) +ΔR)+w_(M) u _(M)(M _(T) ,M _(t) +ΔM)   Equation 18where:

-   -   w_(N)=sales volume utility weight;    -   u_(N)=sales volume utility function;    -   N_(T)=sales volume target;    -   N_(t)=real time achieved sales volume across multiple        deal/volume boards before the auction;    -   w_(R)=revenue utility weight;    -   u_(R)=revenue utility function;    -   R_(T)=revenue target;    -   R_(t)=real time achieved revenue across multiple deal/volume        boards before the auction;    -   w_(M)=profit utility weight;    -   u_(M)=profit utility function;    -   M_(T)=profit target; and    -   M_(t)=real time achieved profit across multiple deal/volume        boards before the auction.

The optimal bid price and offer duration is then determined as:

$\begin{matrix}{\arg\;{\max\limits_{p,d}{\left( {\Delta\; u} \right).}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

The expected sales volume ΔN can be used as number of units to offer forthe auction. Additional constraints can be applied to the optimization,such as limited inventory, supplier's break and so on.

EXAMPLE

Volume board auctions may be modeled as multi-unit auctions. If a sellerwins a volume board auction at price p, all members with commitmentprices higher than p have to commit. So the bid-response function isde-composed into two parts:ρ(p,i)=ρ_(p)(p,i)ρ_(w)(p)   Equation 20where:

-   -   ρ=bid price;    -   i=Blended Consumer Index;    -   ρ_(p)=proportion of the members whose commitment price is higher        than p, and it is the proportion of the members who have to        commit if one of the sellers wins at price p; and    -   ρ_(w)=winning probability of the sellers against all competitors        at price p.

Suppose an initiating volume board has a volume of N. The expected salesvolume from the volume board is:ΔN=Nρ   Equation 21the expected revenue from the volume board is:ΔR=Nρp   Equation 22and the expected profit is calculated asΔM=Nρ(p−c)   Equation 23where c is the unit cost.

There may be multiple (“K”) active Volume Boards for the same product atany time. The number of units (volume) for each board is N_(i) (i=1, 2,. . . , K), and the auction price is P_(i) (i=1, 2, . . . , K). Theprice distributions for the active Volume Boards are ρ₁, ρ₂, . . . ,ρ_(K). In such cases, an optimal auction price p is calculated for anupdated or newly added volume board with having N requests and pricedistribution p for the same product.

The expected sales volume for the product from all active volume boardsis:

$\begin{matrix}{{\Delta\; N} = {{N\;\rho} + {\sum\limits_{i = 1}^{K}{N_{i}\rho_{i}}}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$the expected revenue for the product from all active volume boards is

$\begin{matrix}{{\Delta\; R} = {{N\;\rho\; p} + {\sum\limits_{i = 1}^{K}{N_{i}\rho_{i}P_{i}}}}} & {{Equation}\mspace{14mu} 25}\end{matrix}$and the expected profit is calculated as

$\begin{matrix}{{\Delta\; M} = {{N\;{\rho\left( {p - c} \right)}} + {\sum\limits_{i = 1}^{K}{N_{i}{\rho_{i}\left( {P_{i} - c} \right)}}}}} & {{Equation}\mspace{14mu} 26}\end{matrix}$where c is the unit product cost.

The total expected contribution to the seller's utility is formulated asΔu=w _(N) u _(N)(N _(T) ,N _(t) +ΔN)+w _(R) u _(R)(R _(T) ,R _(t) +ΔR)+w_(M) u _(M)(M _(T) ,M _(t) +ΔM)   Equation 27where:

-   -   w_(N)=sales volume utility weight;    -   u_(N)=sales volume utility function;    -   N_(T)=sales volume target;    -   N_(t)=achieved sales volume before the auction;    -   w_(R)=revenue utility weight;    -   u_(R)=revenue utility function;    -   R_(T)=revenue target;    -   R_(t)=achieved revenue before the auction;    -   w_(M)=profit utility weight;    -   u_(M)=profit utility function;    -   M_(T)=profit target; and    -   M_(t)=achieved profit before the auction.

The optimal bid price may then be determined as

$\begin{matrix}{\arg\;{\max\limits_{p}{\left( {\Delta\; u} \right).}}} & {{Equation}\mspace{14mu} 28}\end{matrix}$

Additional constraints can be applied to the optimization, such aslimited inventory, supplier's break and so on. The multi-levelsimultaneous optimization technique may be also applied to volume board,as illustrated in FIG. 7A.

Sellers can also use a subscriber's personal profile as aconsumer-specific index to identify and serve targeted, personalizedadvertisements to the subscriber. In some cases, the seller may alsoconsider subscriber's community profile (a “community” index) whendetermining whether to participate in a transaction. For example, it maybe more beneficial for a seller to target a subscriber with more friendsthan another subscriber that has an otherwise similar profile, becauseaccess to more subscribers provides additional opportunities to sharedeals and convert sales through the deal and volume boards. Benefitsalso include reduced customer acquisition cost, increased sales revenueand profit.

For example, in traditional, non-SNP-based online channels in whichsellers pay a “cost per click” C and see a conversion ratio ρ, thecustomer acquisition cost is calculated as

$\begin{matrix}{C_{acq} = {\frac{C}{\rho}.}} & {{Equation}\mspace{14mu} 29}\end{matrix}$

However, using the techniques and systems described herein, the customeracquisition cost becomes

$\begin{matrix}{C_{acq} = \frac{C}{N\;\rho}} & {{Equation}\mspace{14mu} 30}\end{matrix}$where N is number of members in a micro-community for a deal board, ornumber of requests in a volume board. As such, propagation using thedeal and volume boards reduces customer acquisition cost by a factor ofN, which can grow exponentially.

Finally the customer acquisition cost can be factored into profitcalculation:ΔM=ΔN(p−c)+f(N,p,d,T)−C _(acq) Nρ.   Equation 31

In addition to reduce customer acquisition cost, deal and volume boardsprovide other business opportunities for sellers. When a subscriberclicks a deal link, the seller can present other messages, such as moreproduct information and up sell/cross sell offers.

The dynamic nature of the deal board presents several challenges indesigning a system architecture that satisfies the deal boardrequirements as well as scales to the magnitude of a typical socialnetwork. For example, an SNP may have 100 million subscribers, each witha micro-community of 100 members. Assuming ten thousand unique productsfor sale, and each subscriber participating in one primary transactionand one secondary transaction, the platform would need to support 100million deal boards, with each deal board containing 200 items (100 fromeach micro-community member's primary transactions and 100 from eachmicro-community member's secondary transactions). As a result, thesystem manages up to 20 billion item/transaction pairs (referred to as a“pair”) across the SNP. Moreover, for popular items, and during busyshopping seasons, multiple subscribers are likely to purchase multipleitems in short periods of time. Therefore, if 1 million subscribers areparticipating in transactions at any given time, the system updates 100million deal boards, all in real-time. This translates into extremelylarge volumes of data, messages, and timers—much larger than can behandled by typical database systems and transaction processing systems.Each pair includes product information, available volume, expirationtime, seller's information, as well as other information about theproduct and the transaction.

In another embodiment, a hardware-centric architecture as describedbelow may be used. Given the massive number of simultaneous operationsrequired, independent, and/or related, for millions of deal boardsspread across the entire SNP, conventional hardware and softwaresolutions may have insufficient processing and/or storage capabilities.As such, the system may, in some embodiments, use a configurable,cell-based multi-dimensional distributed architecture (“C2MDarchitecture”) which leverages the virtual proximity of each subscriberto members of its micro-community. In the typical SNP environment, eachsubscriber connects only to the members (each being a “neighbor”) in herown micro-community (a “neighborhood”) and those subscriberssubsequently connect to other subscribers within their own respectivemicro-communities, and so on.

Referring to FIG. 16, and using the C2MD architecture, each subscriberis mapped to a cell 1605, and each cell along with its own neighborhoodtogether reside on a conceptual plane (also referred as “dimension”),1610. The subscriber resides on a plane in two ways, either as a mastercell 1615 surrounded by its own neighbors, or as a neighbor to eachmember of its micro-community in their respective dimensions. A cell inthis architectural approach represents a number of storage elements suchas several locations of a memory array, a register, or variationsthereof.

Each cell is configured with rules on how to respond (for example,process the presented data in a specific manner and/or propagateinformation to its neighbors) to each specific event (e.g., a change ina cell's status) without requiring any centralized intervention. Otherrules may reside in another memory along with special-purpose hardware.At each event, cell content to be operated upon and any correspondingrules are retrieved and the operations are performed using specializedfunctional units, integrated or otherwise, such as Adders, Multipliers,Bit Shifters, Exclusive-or, Counters and others as needed, and theresulting data is stored back at the cell. Multiple cells can beprocessed simultaneously, especially if the same event is impactingmultiple neighboring cells and same rule is to be applied thusexpediting the processing time rather dramatically. Rules are eitherreversible or irreversible. If reversible, the output is processed usingthe inverse function which decomposes it back into its originalcomponents by reversing the earlier operation, in effect, the outputbecomes the input, and input terminals become the output. Thisreversibility capability is highly efficient and has many applications,some of which are described below.

As an example, an SNP having 10 million subscribers will have 10 millionunique planes, each one representing a subscriber and her ownmicro-community. Each cell in a plane is shared with an additionalnumber of unique dimensions, and the number of dimensions is equivalentto the number of members in its own micro-community. For example, amicro-community having n members will result in n+1 dimensions(including the one in which the master cell resides) which intersect thesubscriber's cell. In one instance, each cell may reside in multi-portmemory in which each port intersects with a dimension, or alternativelya hierarchal arrangement of multi-port memories may be used in which asubset of multiple dimensions are mapped on each port. In anotherembodiment, each of the n intersecting dimensions can be mapped for thesubscriber cell on the remaining dimension containing master cell,residing in a memory array.

As an example, a master cell in a plane is labeled as Cell 1 (alsoreferred as C1). If the cell is located on plane 45 it is specified as(P45, C1). Therefore, this same subscriber may be labeled somethingother than C1 when residing as a neighborhood cell in a plane of it'smicro-community members. For example, if the subscriber was listed inplane 48 it is specified as (P48, C7), assuming its mapped on C7.

In an implementation in which the subscriber initiating a transactionhas 99 neighbors in its micro-community (thus total 100 cells includingthe initiating subscriber on this plane), each of the 99 neighbor cellsin the initiating subscriber's plane has its own unique dimension. Ifeach such neighbor also has 63 members in their own respectivemicro-communities (64 including itself), then each correspondingdimension will have 63 neighboring cells.

Referring to FIG. 17, when a subscriber having a cell located at (P1,C1) consummates a transaction, her cell is updated with the requisiteinformation such as the deal price, deal validity duration, and numberof units, etc. This event (called the “first event”) triggers theexecution of a corresponding rule for the event, in this case topropagate the information to all neighboring cells (numbered 2 to 100)residing on the same plane (plane 1). Execution of the rule changes thestatus of each of the neighborhood cells in plane 1, and the subscribersassociated with those cells to initiate a secondary transaction. In someinstances, further propagation of the information within its ownneighborhood plane is prohibited unless the neighbor cell initiates asecondary transaction for this deal itself. If a secondary transactionis initiated at a neighboring cell (P1, C2) the corresponding rule forthis event requires that deal information, including parameters (e.g.,deal validity duration) be propagated to its 63 neighboring cells (oneeach for members of its own micro-community) residing on plane 2, andthe initiator cell on plane 1 (P1, C1) is updated with a reducedinventory status.

In this example cell (P1, C2) represents the same subscriber as cell(P2, C1). However, in plane 1 it is part of micro-community of anothersubscriber (represented by (P1, C1)), whereas it is the “master cell” ofplane 2 and all of its neighbors in that plane represent members of itsmicro-community. Continuing with the example, other events may alsooccur simultaneously with this second event. For example, a thirdtransaction may be consummated by the cell C50 at plane 1 (P1, C50),which implicates similar rules but to a different “neighborhood” ofcells, resulting in updating of all 63 cells on plane 50 and also theinitiator cell (P1, C1), if, for example, the seller's inventory haschanged. Similarly, if another cell on plane 2 (e.g., (P2, C9))subsequently conducts a transaction (the fourth transaction so far) thepreviously described rule is reapplied, and all cells in itsneighborhood residing on plane 109 (that subscriber's micro-community)are updated, along with cell (P2, C1) on plane 2, the cell where thetransaction originated. As described above, changes in cell (P2, C1)(representing the same subscriber as cell (P1, C2) results in executionof a rule to update the initiator represented by cell (P1, C1) as aneighboring cell to (P1, C2).

Referring to FIG. 18, one embodiment of a hardware-based implementationof the C2MD includes a CPU 1805, cell storage memory 1810, rules memory1815 and a controller 1820, all connected via a common bus 1825 fortransferring data among the components.

The CPU 1805 configures the cell storage memory 1810, the controller1820 and the rules memory 1815 via the common bus 1825 and communicateswith each of these elements as needed. For example, the CPU maydetermine that certain rules require updating, or a particular event hasoccurred that necessitates an update to cell data

Cell storage memory 1810 may be implemented as a multi-port memoryregister for storing each subscriber's cell information, along withpointers to each of the cell's neighbors. The rules memory 1815 containsrules governing the functions to be performed by the CPU 1805 based onpotential inputs. The rules member 1815 also communicates with thecontroller 1820, which may be implemented as a multi-port unit, havingsub-elements such as temporary storage 1830 to store data retrieved fromneighboring cells and as well as functional units 1835 for carrying outcomputational tasks.

For example, when a subscriber initiates a transaction, the subscriber'scell data is updated in the corresponding cell storage memory 1810 andthe controller 1820 is notified of the change in status. The controller1820 then retrieves the pointers to this subscriber's neighboring cells,and subsequently retrieves the corresponding cell data from the cellstorage memory via the multi-port interfaces and stored in its temporaryoperand storage 1830. In parallel, rules are retrieved from the rulesmemory 1810 and applied on each neighbor's cell in the storage 1830 bythe corresponding functional units 1835. Each neighboring cell data inthe cell storage memory is then updated with the results.

The above-described architecture may have many variants andalternatives. For example, in one such alternative, subscriber cells maynot include pointers to neighbors, but instead use a single pointer toan address map table that stores actual neighbor pointers.

This neighborhood rule-based methodology can be executed simultaneouslyacross a large number of dimensions without adversely impacting overallsystem performance and continues to executed from plane to plane untilno additional transactions are initiated or all deal validity durationtimers expire. This neighborhood-based, rule-driven C2MD architecture isespecially suited for applications in which the impact of a change in acell is confined to its virtual or physical neighbors because itfacilitates rapid, simultaneous processing and information flow among avery large number of cells without centralized assistance. Thearchitecture also provides flexibility, as the cell rules areuser-configurable. For example, a rule may be designed to limitavailable inventory at each cell (e.g., on a subscriber-by-subscriberbasis), as opposed to a singular rule limiting inventory for an entirepool of subscribers. Further, multiple, distinct rules may exist andoperate at the same cell for similar yet independent deal flow events.For example, a subscriber may participate in 20 independent,simultaneous deals either as primary or secondary transactions, withdifferent rulesets governing each deal.

Using the C2MD architecture, a transient virtual community consisting ofmany micro-communities can be rapidly created, and deals within thosecommunities processed. Once completed, the virtual community isdisbanded. Millions of such virtual broader communities can be formed ontransient basis simultaneously executing a massive number of tasks, thuseliminating processing bottlenecks encountered by existingtechnologies/architectures.

In various embodiments the CPU and controller may be provided as eithersoftware, hardware, or some combination thereof. For example, the systemmay be implemented on one or more server-class computers, such as a PChaving a CPU board containing one or more processors such as the Pentiumor Celeron family of processors manufactured by Intel Corporation ofSanta Clara, Calif., the 680×0 and POWER PC family of processorsmanufactured by Motorola Corporation of Schaumburg, Ill., and/or theATHLON line of processors manufactured by Advanced Micro Devices, Inc.,of Sunnyvale, Calif.

The cell storage memory and rules memory may include random accessmemory (RAM), read only memory (ROM), and/or FLASH memory residing oncommonly available hardware such as one or more application specificintegrated circuits (ASIC), field programmable gate arrays (FPGA),electrically erasable programmable read-only memories (EEPROM),programmable read-only memories (PROM), programmable logic devices(PLD), or read-only memory devices (ROM). In some embodiments, theprograms may be provided using external RAM and/or ROM such as opticaldisks, magnetic disks, as well as other commonly storage devices.

For embodiments in which the invention is provided as a softwareprogram, the program may be written in any one of a number of high levellanguages such as FORTRAN, PASCAL, JAVA, C, C++, C#, LISP, PERL, BASICor any suitable programming language.

Variations, modifications, and other implementations of what isdescribed herein will occur to those of ordinary skill in the artwithout departing from the spirit and the scope of the invention asclaimed. Accordingly, the invention is to be defined not by thepreceding illustrative description but instead by the spirit and scopeof the following claims.

What is claimed is:
 1. A computerized method for facilitating sales ofgoods to subscribers to a social network platform, the method comprisingthe steps of: receiving, at an application server, an electronic messagefrom a subscriber to a social network platform, the message includingdesired auction parameters of a proposed purchase of an item, theparameters including a final auction time; mapping informationrepresenting each subscriber of the social media network to cells withina cell-based multi-dimensional architecture (C2MD), wherein the C2MD hasa cell storage memory, and wherein information representing thesubscriber is mapped to a master cell on a plane having cells of othersubscribers with connections from the subscriber within the socialnetwork platform, and mapped to a non-master cell on planes havingmaster cells of other subscribers connected to the subscriber within thesocial network platform; providing the desired auction parameters to aninitial set of automated auction engines, each being associated with arespective seller; iteratively receiving, from at least a subset of theinitial set of auction engines, offers to fulfill the proposed purchase,wherein each iteration comprises programmatically-calculated updatedauction parameters increasingly favorable to the subscriber until atleast one engine has provided an initial set of auction parameters;distributing over an electronic network the initial set of auctionparameters to secondary subscribers to the social network platform, thusallowing the secondary subscribers to respond with respectiveindications of interest in joining the proposed purchase, wherein thesecondary subscribers are identified through the C2MD and wherein atleast one of the secondary subscribers has a cell on a same plane as acell of the subscriber within the cell storage memory; receiving at theapplication server indications of interest to join the proposed purchasefrom at least a subset of the secondary subscribers; and upon receipt ofthe indications of interest from the subset of the secondarysubscribers, notifying an updated set of automated auction engines, eachbeing associated with a respective seller, of the indications ofinterest to join the proposed purchase and iteratively receiving, fromat least a subset of the updated set of automated auction engines,offers to fulfill the proposed purchase, wherein each iterationcomprises programmatically-calculated interim auction parametersincreasingly favorable to the secondary subscribers and satisfying aseller-specific objective function constrained by one or moreseller-specific parameters, until a stopping criteria is met.
 2. Themethod of claim 1 wherein the stopping criteria comprises reaching thefinal auction time.
 3. The method of claim 1 wherein the desired auctionparameters comprise one or more of a description of the item to bepurchased, a quantity of the item to be purchased, a number of thesecondary subscribers that may join the proposed purchase, desiredcharacteristics of the secondary subscribers that may join the proposedpurchase, a desired class of seller, delivery options and a returnpolicy.
 4. The method of claim 3 wherein the desired auction parametersfurther include a dream price, and the stopping criteria comprises oneof the interim auctions offering the item at the dream price.
 5. Themethod of claim 4 wherein the dream price is concealed from theautomated auction engines.
 6. The method of claim 3 wherein the desiredauction parameters further include a commitment price.
 7. The method ofclaim 1 wherein the indications of interest to join the proposedpurchase are binding.
 8. The method of claim 1 further comprisingfacilitating the completion of transactions between a seller associatedwith the winning auction engine and the subset of the secondarysubscribers according to a final set of auction parameters, the finalset of auction parameters being the most favorable parameters to thesecondary subscribers offered by one of the updated set of auctionengines.
 9. The method of claim 1 wherein notification to the updatedset of automated auction engines occurs after receipt of each indicationof interest to join the purchase.
 10. The method of claim 1 whereinnotification to the updated set of automated auction engines occursafter receipt of a predefined number of indications of interest to jointhe purchase.
 11. The method of claim 1 wherein the seller-specificparameters comprise one or more of an actual inventory level, desiredinventory level, supplier break, revenue target and profit target. 12.The method of claim 1 wherein the seller-specific objective function isfurther constrained by the number of received indications of interest tojoin the proposed purchase.
 13. The method of claim 1 further comprisingaccessing, by the application server, consumer profiles of the secondarysubscribers and providing the consumer profiles to each auction enginesuch that the profiles are considered when determining whether theauction engines calculate the interim auction parameters.
 14. The methodof claim 1 further comprising providing an electronically renderedscreen to the secondary subscribers, the screen comprising one or moreof the final auction time, dream price, commitment price, desiredauction parameters, initial set of auction parameters, and updatedauction parameters.
 15. The method of claim 1 wherein theseller-specific objective function is further constrained by othertransactions for the item initiated by a member of the social networkplatform other than the subscriber.
 16. The method of claim 1 whereinthe seller-specific objective function is further constrained by othertransactions for items other than the item but supplied by a commonsupplier.
 17. The method of claim 1 wherein each of the secondarysubscribers comprise members of the subscriber's community within thesocial network platform and are one degree of separation removed fromthe subscriber.
 18. The method of claim 1 wherein the secondarysubscribers includes members of the subscribers community within thesocial network platform who are a configurable degree of separationremoved from the subscriber.
 19. The method of claim 1 furthercomprising facilitating a search by the secondary subscribers ofproposed purchases, thereby allowing the secondary subscribers to jointhe proposed purchase.